comparison_norm_frob_sym

Benchmark Report

Frobenius norm with 100 elements and size = 1,000 for a symmetrical matrix

name | rank | runs |      mean |     stdev | baseline

---------|------|------|-----------|-----------|--------- cysparse | 1 | 100 | 2e-06 | 5.985e-07 | 1 pysparse | 2 | 100 | 2.258e-06 | 6.298e-07 | 1.129

Frobenius norm with 1,000 elements and size = 10,000 for a symmetrical matrix

name | rank | runs |      mean |     stdev | baseline

---------|------|------|-----------|-----------|--------- cysparse | 1 | 100 | 1.296e-05 | 4.309e-07 | 1 pysparse | 2 | 100 | 2.415e-05 | 0.0001057 | 1.863

Frobenius norm with 10,000 elements and size = 100,000 for a symmetrical matrix

name | rank | runs |      mean |     stdev | baseline

---------|------|------|-----------|-----------|--------- cysparse | 1 | 100 | 0.0001219 | 3.315e-06 | 1 pysparse | 2 | 100 | 0.0001236 | 2.942e-06 | 1.014

Frobenius norm with 80,000 elements and size = 100,000 for a symmetrical matrix

name | rank | runs |      mean |     stdev | baseline

---------|------|------|-----------|-----------|--------- pysparse | 1 | 100 | 0.0001494 | 4.654e-06 | 1 cysparse | 2 | 100 | 0.0001496 | 4.518e-06 | 1.001

Total runs: 800 run in arbitrary order Python version: 2.7.5+ System: x86_64 Linux 3.11.0-12-generic Version: benchmark v0.1.5 Date: 2015-05-28 21:40:43